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Electromagnetism & Resonant Recognition Model
The interaction between biomacromolecules dependent on their electromagnetic resonance

Pablo Andueza Munduate

The Resonant Recognition Model (RRM) is a theory originally proposed by Cosic et al [1] and that has begun to be tested experimentally [2-5, 12-15] where it's considered a resonant electromagnetic energy and information transfer between interacting biomolecules (for example proteins), specific for each function, that is calculated considering the energy of delocalised electrons of each constituent (for example amino acids). ...

This theory predicts that macromolecular activity is based on electromagnetic resonances, the delocalised electrons moving along macromolecular (protein, DNA, RNA) backbone-like helical structure, can produce electromagnetic radiation, absorption and resonance with spectral characteristics corresponding to energy distribution along macromolecule. The calculus is done as described in [2], here is an introduction of it:

" All proteins, DNA and RNA can be considered as a linear sequence of their constitutive elements: amino acids or nucleotides. The RRM model interprets this linear information as a numerical series by assigning each amino acid a physical parameter representing the energy of delocalised electrons of each amino acid and then transforming this numerical series into the frequency domain using Fourier Transform."

They discovered that each specific biological function within the protein or DNA is characterized by one frequency, that all protein sequences with the common biological function have a common frequency component related to the protein biological function, and that proteins and their targets have the same characteristic frequency in common [1]. Those protein and DNA resonances are within infra-red, visible and small portion of ultraviolet light [16].

In a review by W. Jaross [17] some aspects of this resonance recognition of the vibration patterns by proteins as precondition for molecular interactions are discussed:

" The molecular vibration patterns of structure-forming macromolecules in the living cell create very specific electromagnetic frequency patterns which might be used for information on spatial position in the three-dimensional structure as well as the chemical characteristics. Chemical change of a molecule results in a change of the vibration pattern and thus in a change of the emitted electromagnetic frequency pattern. These patterns have to be received by proteins responsible for the necessary interactions and functions. Proteins can function as resonators for frequencies in the range of 1013-1015 Hz. The individual frequency pattern is defined by the amino acid sequence and the polarity of every amino acid caused by their functional groups. If the arriving electromagnetic signal pattern and the emitted pattern of the absorbing protein are matched in relevant parts and in opposite phase, photon energy in the characteristic frequencies can be transferred resulting in a conformational change of that molecule and respectively in an increase of its specific activity."

The same author also proposes some mechanism that can facilitate to overcome intracellular distances:

" The oscillation of polarized structural elements such as cell membrane rafts or microtubules (12, 25-27) might be able to produce appropriate carrier frequencies able to overcome intracellular distances. The frequencies of MT oscillations have been found to be in the range from THz down to KHz (10, 11). The MT structure itself could also be the base for forwarding the IR-photons with a minimal loss of energy if they are used as waveguides. The quasi-crystalline ordered water molecules inside and outside of the charged tubular structure of the MT could promote that (see Funk, this issue)."

The theory has been corroborated by some experimental findings, for example Murugan et al. [3] shows that evola strains that are lethal and not lethal have different electromagnetic emissions (that also are named biophotonic emissions because of their wavelengths within the visible or near-visible light band, there is a section [4] dedicated to biophotons in this web), and that those emissions can be calculated very accurately (with about 10 nm of error margin) using the Resonant Recognition Model. Also it has been predicted processes involved in the interactions of Zika virus (ZIKV) with the human host suggesting the exchange of electromagnetic radiation at the frequencies of 601.8nm (yellow light) and 1203.6nm (near infrared) during ZIKV envelope protein with the AXL receptor in the human tissue [14]. In this sense is also interesting to note a fruitful attempt to detect Hepatitis C Virus [9] based on electromagnetic detection of it’s resonant frequency (as predicted by Cosic).

In [12] genes and protein related to development of breast cancer are analyzed under the scope of RRM being this capable to analyze protein biological functions/interactions and predict bioactive mutations. Also the fluctuating wavelengths of biophotonic emissions (or ultraweak photon emissions (UPE)) from stressed cancer cells can be calculated based on this theory [18].

Proteins interactions themselves have been also viewed through RRM, for example with the PKCζ and PKMζ brain specific protein kinases [13] (that have a role in memory consolidation mechanisms):

" PKCζ can be viewed as a resonance system emitting/receiving infrared light 3190 nm. PKMζ is much more active at this main wavelength, and also is tuned to near infrared at 913 nm. The regulatory and hinge domains are tuned to yellow light (609 nm). At the same time, they do interact with PKMζ through infrared light 3190 nm."

Even complete cellular signaling pathways can be described in this electromagnetic resonance terms, Karbowki et al. [4] wrote for the classic JAK–STAT signaling pathway:

" Several experimental studies have verified the predicted peak wavelength of photons within the visible or near-visible light band for specific molecules. Here, this concept has been applied to a classic signaling pathway, JAK–STAT, traditionally composed of nine sequential protein interactions. The weighted linear average of the spectral power density (SPD) profiles of each of the eight “precursor” proteins displayed remarkable congruence with the SPD profile of the terminal molecule (CASP-9) in the pathway. These results suggest that classic and complex signaling pathways in cells can also be expressed as combinations of resonance energies."

The same occur for the classic ERK-MAP signaling pathways between the plasma cell membrane and the nucleus [5]:

" Spectral analyses of sequences of pseudopotentials that reflect de-localized electrons of amino acids for the 11 proteins in the pathway were computed. The spectral power density of the terminal protein (cFOS) was shown to be the average of the profiles of the precursor proteins. The results demonstrated that in addition to minute successive alterations in molecular structure wave-functions and resonant patterns can also describe complex molecular signaling pathways in cells. Different pathways may be defined by a single resonance profile."

Cosic, based on her theory also proposed some therapeutic applications, for example to neutralize malaria parasite [5]. In this website a section [6] is disposed dedicated to low level light therapy and experiments where light is applied to achieve different biological responses and where the mechanism by which those effects are provoked can be related in most cases to resonant effects like those predicted by the RRM, moreover there are two sections [7,8] where the listed papers specifically attribute the effects to consequences derived from RRM.

It must be said that there are some alternative resonance models that also are going to be included in this section, for example [10] propose a model that differs a bit to that of RRM:

" Compared to this previous work, our contribution is twofold. First, whereas the determination of RRM-based hotspots initially requires the computation of the characteristic frequency of a family of proteins, we do not impose such a constraint. Second, rather than a purely DSP-based approach as in [2], [14]–[17] aimed at detecting local residues associated with the characteristic frequency, we combine DSP tools and mutagenesis principles."

Or the model of [11] that modified RRM by using the wavelet transform.

The importance of the electromagnetic resonant recognition between macromolecules in regard to an electromagnetic mind theory is that those recognition fields are another layer of the electromagnetic mind, being this more general and multi-layered.

References:

1. Ćosić, Irena, et al. "Electromagnetic properties of biomolecules." FME Transactions 34.2 (2006): 71-80.

2. Cosic, Irena, Drasko Cosic, and Katarina Lazar. "Is it possible to predict electromagnetic resonances in proteins, DNA and RNA?." EPJ Nonlinear Biomedical Physics 3.1 (2015): 1.

3. Murugan, Nirosha J., Lukasz M. Karbowski, and Michael A. Persinger. "Cosic’s Resonance Recognition Model for Protein Sequences and Photon Emission Differentiates Lethal and Non-Lethal Ebola Strains: Implications for Treatment." Open Journal of Biophysics 5.01 (2014): 35.

4. Karbowski, Lukasz M., Nirosha J. Murugan, and Michael A. Persinger. "Novel Cosic resonance (standing wave) solutions for components of the JAK–STAT cellular signaling pathway: A convergence of spectral density profiles." FEBS open bio 5.1 (2015): 245-250.

5. Cosic, Irena, JoseLuis Hernandes Caceres, and Drasko Cosic. "Possibility to interfere with malaria parasite activity using specific electromagnetic frequencies." EPJ Nonlinear Biomedical Physics 3.1 (2015): 1.

6. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects ›

7. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects › Resonant Recognition Model - Infrared

8. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects › Light - Resonant Recognition Model

9. Shiha, G., et al. "1174 A Novel Method for Non-Invasive Diagnosis of Hepatitis C Virus Using Electromagnetic Signal Detection: A Multicenter International Study." Journal of Hepatology 58 (2013): S477.

10. Nguyen, Quang-Thang, Ronan Fablet, and Dominique Pastor. "Protein interaction hotspot identification using sequence-based frequency-derived features." IEEE Transactions on Biomedical Engineering 60.11 (2013): 2993-3002.

11. Liu, Xiang, and Yifei Wang. "A modified resonant recognition model to predict protein-protein interaction." Frontiers of Biology in China 2.3 (2007): 268-271.

12. Cosic, Irena, Drasko Cosic, and Katarina Lazar. "Cancer Related BRCA-1 and BRCA-2 Mutations as Analysed by the Resonant Recognition Model." Journal of Advances in Molecular Biology 1.2 (2017).

13. García I, S. V., & Hernández-Cáceres I, J. L. (2017). Studying Protein kinases PKCζ and PKMζ with the Resonant Recognition Model. Implications for the study of Memory Mechanisms. Revista Cubana de Informática Médica, 9(2), 121-134.

14. Cáceres, J. L. H., & Wright, G. (2018). Zika Virus Viewed Through the Resonant Recognition Model. Unraveling New Avenues for Understanding and Managing a Serious Threat. EJBI, 14(1), 11-17.

15. Almansour, Nahlah M., et al. "A bioactive peptide analogue for myxoma virus protein with a targeted cytotoxicity for human skin cancer in vitro." Journal of biomedical science 19.1 (2012): 65.

16. Cosic, Irena, Drasko Cosic, and Katarina Lazar. "Tesla, Bioresonances and Resonant Recognition Model." Second International Congress Nikola Tesla-Disruptive innovation. 2017.

17. Jaross, Werner. "Hypothesis on interactions of macromolecules based on molecular vibration patterns in cells and tissues." Frontiers in bioscience (Landmark edition) 23 (2018): 940-946.

18. Dotta, Blake T. "Cosic's Resonant Recognition Model for Macromolecules can be used to Predict and Modify the Fluctuating Wavelengths of Ultraweak Photon Emissions from Stressed Cancer Cells." Biophysical Journal 106.2 (2014): 183a.

Very related sections:

expand this introductory text

text updated: 13/09/2018
tables updated: 25/02/2018

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